ssd [1]} is a single-shot detector for multiple categories within the one-stage paradigm that effectively combines ideas from rpn in Faster R-CNN [2]}, yolo [3]} and multiscale convolutional features [4]}. Like yolo, SSD predicts a fixed number of bounding boxes and objectness scores, followed by nms to filter out the final detections. The backbone is based on standard classification cnn, like VGG16 [5]}, truncated before any classification layers. Then, several auxiliary convolutional layers, progressively decreasing in size, are added on top of it. Since the information in the last layer with low resolution may be too coarse spatially to allow precise localization, SSD uses shallower layers with higher resolution for detecting small objects. More, for objects of different sizes, SSD performs detection over multiple scales by operating on multiple convolutional feature maps, each of which predicts category scores and box offsets for bounding boxes of appropriate sizes.
